Data Science Job Roles

Data science is a "concept to unify statistics, data analysis, machine learning, domain knowledge and their related methods" in order to "understand and analyze actual phenomena" with data.[1] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science.

“Data scientist” is often used as a blanket title to describe jobs that are drastically different. Let’s looks at four kind of data science jobs.

1. Data Analyst

There are some companies where being a data scientist is synonymous with being a Data Analyst. A Data Analyst interprets data and turns it into information which can offer ways to improve a business, thus affecting business decisions. Data Analysts gather information from various sources and interpret patterns and trends. A job might consist of tasks like pulling data out of SQL databases, becoming an Excel or Tableau master, and producing basic data visualizations and reporting dashboards.

2. Data Engineer

Some companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they start looking for someone to set up a lot of the data infrastructure that the company will need moving forward. Data Engineers are those data professionals who prepare the “big data” infrastructure. They are software engineers who design, build, integrate data from various resources, and manage big data.

3. Machine Learning Engineer

There are a number of companies for whom their data (or their data analysis platform) is their product. This is probably the ideal situation for someone who has a formal mathematics, statistics, or physics background and is hoping to continue down a more academic path. Machine learning engineers design self-running software to automate predictive models. ML encompasses deep learning (DL) and that subfield uses artificial neural networks to “think” and solve complex problems with multi-layered (deep) data sets.

4. Data Scientist

A lot of companies care about data but probably isn’t a data company (i.e. their data is not their actual product). Data scientists will get data that has passed a first round of cleaning and manipulation, which they can use to feed to sophisticated analytics programs and machine learning and statistical methods to prepare data for use in predictive and prescriptive modeling. Of course, to build models, they need to do research industry and business questions, and they will need to leverage large volumes of data from internal and external sources to answer business needs. This also sometimes involves exploring and examining data to find hidden patterns.

Here’s a look at typical must-have skills required for each of the roles.

[1] Hayashi, Chikio (1 January 1998). "What is Data Science? Fundamental Concepts and a Heuristic Example".